Can epidemiological models predict herd immunity?
Prof. Nico Orce
University of the Western Cape
We have solved the so-called SIR (Susceptible, Infected, Removed) transmission-dynamics equations analytically with (ESIR model) and without (D model) recovery assumptions, characterizing the evolution of pandemic waves at different stages (exponential and slowdown phases, peak, decay, etc). Monte-Carlo simulations also support these pandemic trends. We applied our models to the different pandemic waves worldwide, where similar trends suggest a common pandemic evolution with universal parameters. Although lockdown conditions continuously change, affecting the initial conditions of the transmission-dynamics equations, our models can be extended to describe additional spatial-time effects arising, for instance, from the release of lockdown measures. Additionally, I'll also show how our results may support herd immunity. Videos from previous seminars and mini-schools can be found @ https://www.youtube.com/c/NicoOrce. Our work has been published in Applied Mathematical Modeling (https://doi.org/10.1016/j.apm.2020.10.019).
Hosted by Prof. Brodeur
All interested persons are invited to attend remotely—email firstname.lastname@example.org for information.
Originally published at physics.nd.edu.